Development of Regional Traffic Data for the Mechanistic–Empirical Pavement Design Guide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To obtain full benefits from the new Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures (MEPDG), it is necessary to characterize pavement traffic loads using detailed traffic data, including axle load spectra. Preferably, the detailed traffic data should be site specific. In the absence of site-specific traffic data, default input data need to be used. Truck traffic data, collected as part of a periodic commercial traffic survey, were used to obtain the best possible default values for traffic input parameters required for the MEPDG. Default traffic input parameters were developed for two Ontario, Canada, regions. The sensitivity of the predicted pavement performance to changes in traffic input parameters was explored. There are several notable differences between the default traffic data inputs included in the MEPDG software and the regional traffic data inputs developed for Ontario, particularly in terms of axle load spectra. Axle load spectra for Ontario have a smaller number of heavily overloaded axles, and the peaks between loaded and unloaded axles are more pronounced. There are also notable differences between axle load spectra for northern and southern Ontario. Compared with southern Ontario, northern Ontario axle load spectra are heavier and have a large proportion of fully loaded axles. The number and type of trucks, followed by the axle load spectra, have the predominant influence on the predicted pavement performance. The MEPDG contains several input parameters that do not have any significant influence on the predicted pavement performance, namely, hourly traffic volume adjustment factors and axle spacing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it